Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.

AIMS: Hospitalized patients with heart failure (HF) are a heterogeneous population, with multiple phenotypes proposed. Prior studies have not examined the biological phenotypes of critically ill patients with HF admitted to the contemporary cardiac intensive care unit (CICU). We aimed to leverage unsupervised machine learning to identify previously unknown HF phenotypes in a large and diverse cohort of patients with HF admitted to the CICU. METHODS: We screened 6008 Mayo Clinic CICU patients with an admission diagnosis of HF from 2007 to 2018 and included those without missing values for common laboratory tests. Consensus k-means clustering was performed based on 10 common admission laboratory values (potassium, chloride, anion gap, blood urea nitrogen, haemoglobin, red blood cell distribution width, mean corpuscular volume, platelet count, white blood cell count and neutrophil-to-lymphocyte ratio). In-hospital mortality was evaluated using logistic regression, and 1 year mortality was evaluated using Cox proportional hazard models after multivariable adjustment. RESULTS: Among 4877 CICU patients with HF who had complete admission laboratory data (mean age 69.4 years, 38.4% females), we identified five clusters with divergent demographics, comorbidities, laboratory values, admission diagnoses and use of critical care therapies. We labelled these clusters based on the characteristic laboratory profile of each group: uncomplicated (25.7%), iron-deficient (14.5%), cardiorenal (18.4%), inflamed (22.3%) and hypoperfused (19.2%). In-hospital mortality occurred in 10.7% and differed between the phenotypes: uncomplicated, 2.7% (reference); iron-deficient, 8.1% [adjusted odds ratio (OR) 2.18 (1.38-3.48), P < 0.001]; cardiorenal, 10.3% [adjusted OR 2.11 (1.37-3.32), P < 0.001]; inflamed, 12.5% [adjusted OR 1.79 (1.18-2.76), P = 0.007]; and hypoperfused, 21.9% [adjusted OR 4.32 (2.89-6.62), P < 0.001]. These differences in mortality between phenotypes were consistent when patients were stratified based on demographics, aetiology, admission diagnoses, mortality risk scores, shock severity and systolic function. One-year mortality occurred in 31.5% and differed between the phenotypes: uncomplicated, 11.9% (reference); inflamed, 26.8% [adjusted hazard ratio (HR) 1.56 (1.27-1.92), P < 0.001]; iron-deficient, 33.8% [adjusted HR 2.47 (2.00-3.04), P < 0.001]; cardiorenal, 41.2% [adjusted HR 2.41 (1.97-2.95), P < 0.001]; and hypoperfused, 52.3% [adjusted HR 3.43 (2.82-4.18), P < 0.001]. Similar findings were observed for post-discharge 1 year mortality. CONCLUSIONS: Unsupervised machine learning clustering can identify multiple distinct clinical HF phenotypes within the CICU population that display differing mortality profiles both in-hospital and at 1 year. Mortality was lowest for the uncomplicated HF phenotype and highest for the hypoperfused phenotype. The inflamed phenotype had comparatively higher in-hospital mortality yet lower post-discharge mortality, suggesting divergent short-term and long-term prognosis.

Stiff right atrial syndrome? A complex clinical case report utilizing multimodality imaging and invasive hemodynamics.

Background: Stiff left atrial syndrome is a well-established cause of heart failure symptoms. A parallel entity involving the right atrium (RA) has not previously been described. We present a case of refractory right heart failure (RHF) 12 years following orthotopic heart transplantation. Case summary: Patient underwent annuloplasty ring placement for severe tricuspid regurgitation in 2018 and kidney transplantation in 2020. The use of multimodality imaging and a multidisciplinary approach suggested a stiff RA as a potential etiology to refractory symptoms. Redo-heart and kidney transplantation in March 2021 led to the resolution of symptoms without recurrence. Discussion: We propose stiff right atrial syndrome that may need to be considered in the setting of refractory RHF primarily suggested by significant right atrial enlargement and restrictive physiology.

PROGNOSTIC PERFORMANCE OF SERIAL DETERMINATION OF THE SOCIETY FOR CARDIOVASCULAR ANGIOGRAPHY AND INTERVENTIONS SHOCK CLASSIFICATION IN ADULTS WITH CRITICAL ILLNESS.

ABSTRACT: Purpose: The aim of the study is to evaluate whether serial assessment of shock severity can improve prognostication in intensive care unit (ICU) patients. Materials and Methods: This is a retrospective cohort of 21,461 ICU patient admissions from 2014 to 2018. We assigned the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Stage in each 4-h block during the first 24 h of ICU admission; shock was defined as SCAI Shock stage C, D, or E. In-hospital mortality was evaluated using logistic regression. Results: The admission SCAI Shock stages were as follows: A, 39.0%; B, 27.0%; C, 28.9%; D, 2.6%; and E, 2.5%. The SCAI Shock stage subsequently increased in 30.6%, and late-onset shock developed in 30.4%. In-hospital mortality was higher in patients who had shock on admission (11.9%) or late-onset shock (7.3%) versus no shock (4.3%). Persistence of shock predicted higher mortality (adjusted OR = 1.09; 95% CI = 1.06-1.13, for each ICU block with shock). The mean SCAI Shock stage had higher discrimination for in-hospital mortality than the admission or maximum SCAI Shock stage. Dynamic modeling of the SCAI Shock classification improved discrimination for in-hospital mortality (C-statistic = 0.64-0.71). Conclusions: Serial application of the SCAI Shock classification provides improved mortality risk stratification compared with a single assessment on admission, facilitating dynamic prognostication.